70 research outputs found
A Search for Boosted Low Mass Resonances Decaying to the bbÌ Final State and Produced in Association with a Jet at âs = 13 TeV with the ATLAS Detector
A search in the high momentum regime for new resonances, produced in association with a jet, decaying into a pair of bottom quarks is presented using an integrated luminosity of 80.5 fb-1 of proton-proton collisions at center-of-mass energy âs = 13 TeV recorded by the ATLAS detector at the Large Hadron Collider. The search was performed for low mass resonances, including the Standard Model Higgs boson and leptophobic Z\u27 dark matter mediators, in the mass range of 100 GeV to 200 GeV. For the Standard Model Higgs boson, the observed signal strength is ÎŒH = 5.8 ± 3.1 (stat.) ± 1.9 (syst.) ± 1.7 (th.), which is consistent with the background-only hypothesis at 1.6 standard deviations. No evidence of a significant excess of events beyond the expected background is found and competitive limits on leptophobic Z\u27 boson axial-vector couplings to Standard Model quarks with democratic couplings to all quark generations are set for the mass range considered. The dominant background in this analysis is irreducible multijet events from QCD interactions, which I modeled using a parametric function that was robust to fitting bias and spurious signals
Bayesian Methodologies with pyhf
bayesian_pyhf is a Python package that allows for the parallel Bayesian and
frequentist evaluation of multi-channel binned statistical models. The Python
library pyhf is used to build such models according to the HistFactory
framework and already includes many frequentist inference methodologies. The
pyhf-built models are then used as data-generating model for Bayesian inference
and evaluated with the Python library PyMC. Based on Monte Carlo Chain Methods,
PyMC allows for Bayesian modelling and together with the arviz library offers a
wide range of Bayesian analysis tools.Comment: 8 pages, 3 figures, 1 listing. Contribution to the Proceedings of the
26th International Conference on Computing In High Energy and Nuclear Physics
(CHEP 2023
Distributed statistical inference with pyhf enabled through funcX
In High Energy Physics facilities that provide High Performance Computing
environments provide an opportunity to efficiently perform the statistical
inference required for analysis of data from the Large Hadron Collider, but can
pose problems with orchestration and efficient scheduling. The compute
architectures at these facilities do not easily support the Python compute
model, and the configuration scheduling of batch jobs for physics often
requires expertise in multiple job scheduling services. The combination of the
pure-Python libraries pyhf and funcX reduces the common problem in HEP analyses
of performing statistical inference with binned models, that would
traditionally take multiple hours and bespoke scheduling, to an on-demand
(fitting) "function as a service" that can scalably execute across workers in
just a few minutes, offering reduced time to insight and inference. We
demonstrate execution of a scalable workflow using funcX to simultaneously fit
125 signal hypotheses from a published ATLAS search for new physics using pyhf
with a wall time of under 3 minutes. We additionally show performance
comparisons for other physics analyses with openly published probability models
and argue for a blueprint of fitting as a service systems at HPC centers.Comment: 9 pages, 1 figure, 2 listings, 1 table, submitted to the 25th
International Conference on Computing in High Energy & Nuclear Physic
Deep Learning for the Matrix Element Method
Extracting scientific results from high-energy collider data involves the
comparison of data collected from the experiments with synthetic data produced
from computationally-intensive simulations. Comparisons of experimental data
and predictions from simulations increasingly utilize machine learning (ML)
methods to try to overcome these computational challenges and enhance the data
analysis. There is increasing awareness about challenges surrounding
interpretability of ML models applied to data to explain these models and
validate scientific conclusions based upon them. The matrix element (ME) method
is a powerful technique for analysis of particle collider data that utilizes an
\textit{ab initio} calculation of the approximate probability density function
for a collision event to be due to a physics process of interest. The ME method
has several unique and desirable features, including (1) not requiring training
data since it is an \textit{ab initio} calculation of event probabilities, (2)
incorporating all available kinematic information of a hypothesized process,
including correlations, without the need for feature engineering and (3) a
clear physical interpretation in terms of transition probabilities within the
framework of quantum field theory. These proceedings briefly describe an
application of deep learning that dramatically speeds-up ME method calculations
and novel cyberinfrastructure developed to execute ME-based analyses on
heterogeneous computing platforms.Comment: 6 pages, 3 figures. Contribution to the Proceedings of the ICHEP 2022
Conferenc
Reinterpretation and Long-Term Preservation of Data and Code
Careful preservation of experimental data, simulations, analysis products,
and theoretical work maximizes their long-term scientific return on investment
by enabling new analyses and reinterpretation of the results in the future. Key
infrastructure and technical developments needed for some high-value science
targets are not in scope for the operations program of the large experiments
and are often not effectively funded. Increasingly, the science goals of our
projects require contributions that span the boundaries between individual
experiments and surveys, and between the theoretical and experimental
communities. Furthermore, the computational requirements and technical
sophistication of this work is increasing. As a result, it is imperative that
the funding agencies create programs that can devote significant resources to
these efforts outside of the context of the operations of individual major
experiments, including smaller experiments and theory/simulation work. In this
Snowmass 2021 Computational Frontier topical group report (CompF7:
Reinterpretation and long-term preservation of data and code), we summarize the
current state of the field and make recommendations for the future.Comment: Snowmass 2021 Computational Frontier CompF7 Reinterpretation and
long-term preservation of data and code topical group repor
Software Citation in HEP: Current State and Recommendations for the Future
In November 2022, the HEP Software Foundation (HSF) and the Institute for
Research and Innovation for Software in High-Energy Physics (IRIS-HEP)
organized a workshop on the topic of Software Citation and Recognition in HEP.
The goal of the workshop was to bring together different types of stakeholders
whose roles relate to software citation and the associated credit it provides
in order to engage the community in a discussion on: the ways HEP experiments
handle citation of software, recognition for software efforts that enable
physics results disseminated to the public, and how the scholarly publishing
ecosystem supports these activities. Reports were given from the publication
board leadership of the ATLAS, CMS, and LHCb experiments and HEP open source
software community organizations (ROOT, Scikit-HEP, MCnet), and perspectives
were given from publishers (Elsevier, JOSS) and related tool providers
(INSPIRE, Zenodo). This paper summarizes key findings and recommendations from
the workshop as presented at the 26th International Conference on Computing In
High Energy and Nuclear Physics (CHEP 2023).Comment: 7 pages, 2 listings. Contribution to the Proceedings of the 26th
International Conference on Computing In High Energy and Nuclear Physics
(CHEP 2023
The Scikit HEP Project -- overview and prospects
Scikit-HEP is a community-driven and community-oriented project with the goal
of providing an ecosystem for particle physics data analysis in Python.
Scikit-HEP is a toolset of approximately twenty packages and a few "affiliated"
packages. It expands the typical Python data analysis tools for particle
physicists. Each package focuses on a particular topic, and interacts with
other packages in the toolset, where appropriate. Most of the packages are easy
to install in many environments; much work has been done this year to provide
binary "wheels" on PyPI and conda-forge packages. The Scikit-HEP project has
been gaining interest and momentum, by building a user and developer community
engaging collaboration across experiments. Some of the packages are being used
by other communities, including the astroparticle physics community. An
overview of the overall project and toolset will be presented, as well as a
vision for development and sustainability.Comment: 6 pages, 3 figures, Proceedings of the 24th International Conference
on Computing in High Energy and Nuclear Physics (CHEP 2019), Adelaide,
Australia, 4-8 November 201
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in
high-energy physics (HEP) is well established and pertinent given the complex
detectors, large data, sets and sophisticated analyses at the heart of HEP
research. Moreover, exploiting symmetries inherent in physics data have
inspired physics-informed ML as a vibrant sub-field of computer science
research. HEP researchers benefit greatly from materials widely available
materials for use in education, training and workforce development. They are
also contributing to these materials and providing software to DS/ML-related
fields. Increasingly, physics departments are offering courses at the
intersection of DS, ML and physics, often using curricula developed by HEP
researchers and involving open software and data used in HEP. In this white
paper, we explore synergies between HEP research and DS/ML education, discuss
opportunities and challenges at this intersection, and propose community
activities that will be mutually beneficial.Comment: Contribution to Snowmass 202
Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data
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